Bibliographic record
Abstract
I first met Peter in the early 1970s.I knew of him mainly because he, like me, was a veterinarian who did research and published in international nonveterinary journals.Upon meeting him, which initially occurred when he passed through Saskatoon, Canada, where I had my laboratory in the early 1970s, it was soon apparent that neither of us were cut out to practice our intended craft of fixing diseased creatures.Instead, we were interested in viruses and figuring out how they interacted with their host.Nevertheless, both of us began our research careers working with real animals (i.e., those you eat or become fond of), but soon slipped to working with rodents.Over the years, I met Peter frequently, our families became and remain lifelong friends and I also got to know many, perhaps most, of Peter's trainees.These included some quite amazing characters such as Ralph Tripp and more sane folk such as Woody, Jack Bennink, Rhonda Cardin, Steve Turner, Mark Sangster, and many more.From the earliest years, Peter has been a role model for me.I always envied his common sense understanding of science and his ability to ask penetrating questions often criticizing people and their ideas without them realizing it.On the contrary, I always succeeded in insulting people even when I was trying to be nice!In the 1970s, I got to listen to many of Peter's talks and review some of his grants.Unlike the lucid Peter of today, his scientific stories were not the easiest to follow and his experimentation could be beyond the pale complex.Peter departed the Wistar where he spent several years solidifying his reputation as a viral immunologist and returned to Canberra to head up the Australian National University Department of Pathology.I joined him there for a minisabbatical in 1986.It was obvious that Peter enjoyed bench science but almost despised administration and other administrators (such as Bede Morris, also a veterinarian).He wanted to leave Canberra (''
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".